Hybrid Multi-Objective Methods to Solve Reentrant Shops

International audienceThis article examines the multi-objective scheduling of a reentrant hybrid flow shop. This type of shop is composed of several stages made of several identical parallel machines. When a task has to be processed on a stage, it is assigned to the machine with the smallest workload. This problem shows a reentrant structure: each task must be processed several times at each stage. In this paper, this problem is solved by minimizing two objectives: the makespan (maximum completion time of the jobs) and the total tardiness of the tasks. A new method is improved with different local searches: Adjacent and Non Adjacent Pairwise Interchange, Extract and Backward-Shifted Reinsertion, and Extract and Forward-Shifted Reinsertion. Every local search is tuned with statistical method (design of experiment) and the best one is worked out. This method is compared with the best one in several instances. The results involve three different measures

Multiobjective scheduling of a reentrant hybrid flowshop problem

International audienc

L-Archive to solve multi-objective reentrant hybrid-flowshop scheduling problem

International audienc

Algorithmes pour la résolution du problème d'ordonnancement de tâches réentrantes

National audienc

A mapping technique to improve solutions for a Multi-Objective Resource Constraint Project Scheduling Problem

International audienc

Multi-objective scheduling of a reentrant hybrid-flowshop problem

International audienc

New multi-objective method to solve reentrant hybrid flow shop scheduling problem

International audienceThis paper focuses on the multi-objective resolution of a reentrant hybrid flow shop scheduling problem (RHFS). In our case the two objectives are: the maximization of the utilization rate of the bottleneck and the minimization of the maximum completion time. This problem is solved with a new multi-objective genetic algorithm called L-NSGA which uses the Lorenz dominance relationship. The results of L-NSGA are compared with NSGA2, SPEA2 and an exact method. A stochastic model of the system is proposed and used with a discrete event simulation module. A test protocol is applied to compare the four methods on various configurations of the problem. The comparison is established using two standard multi-objective metrics. The Lorenz dominance relationship provides a stronger selection than the Pareto dominance and gives better results than the latter. The computational tests show that L-NSGA provides better solutions than NSGA2 and SPEA2; moreover, its solutions are closer to the optimal front. The efficiency of our method is verified in an industrial field-experiment

Genetic algorithms hybridized with the self controlling dominance to solve a multi-objective resource constraint project scheduling problem

International audienceThe Resource Constraint Project Scheduling Problem (RCPSP) is one of the most challenged scheduling topics. Compared to the other scheduling problems, the RCPSP pays special attention to the consumable resources with limited capacities, which is the major issue that industry has to cope with. In our study, we tackle a Multi-Objective RCPSP with minimization of the makespan, the total job tardiness and maximization of the workload balance level. Non-dominated Sorting Genetic Algorithm II (NSGAII) and NSGAIII are applied at first to find approximated Pareto fronts. In particular circumstances, decision makers would prefer preselected propositions than the whole Pareto front. For this reason, we have integrated in our study, the Self Controlling Dominance Area of Solutions (SCDAS) in our algorithms find more fine-grained Pareto fronts, and solutions with good qualities on all objectives. Small, medium and large size instances, featured by different parameters of jobs and resources are tested. A comparative study is carried out where the hypervolume and the metric-C are used to evaluate the performances of different methods. The improvements brought by the SCDAS are proved regarding both metrics

Méthodes multi-objectifs pour l’ordonnancement de lignes réentrantes

International audienceThis article presents the scheduling of a reentrant maintenance line with parallel machine stages. In this study the system is composed of machines with their upstream buffer and are modeled by queuing system. The criteria are the maximization of the utilization rate of the bottleneck and the minimization of the mean cycle time of the products. We present the results obtained by a multi-objective ant colony algorithm with local search (MOACS-LS), which are compared with the results obtained by one of the most competitive genetic algorithm called Non-dominated Sorting Genetic Algorithm version 2 (NSGA2). This two metaheuristics are coupled with a discrete event simulation module. Our results are compared with an industrial solution.Cet article présente l’ordonnancement d'une ligne réentrante de maintenance qui comporte une structure à étages regroupant des machines en parallèle. Le système constitué de machines avec leurs stocks amont est modélisé comme un réseau de files d'attente et les critères à optimiser sont la maximisation du taux d'utilisation de la machine goulet, et la minimisation du temps de cycle moyen des produits. Nous mettons ici en évidence les resultants obtenus avec une méthode multi-objectif de colonies de fourmis avec recherche locale (MOACS-LS) que nous comparons avec les résultats obtenus par un algorithme génétique du type NSGA2. Ces deux métaheuristiques sont couplées avec un module de simulation à événements discrets qui permet d'évaluer chaque solution. Nous comparons les resultants obtenus par nos solutions à la solution issue d'un cas industriel en fonction de deux measures différentes

Application de la dominance de lorenz pour l'ordonnacement multiobjectif

International audienc